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Observational records are more often than not influenced by residual non-climatic factors which must be detected and adjusted for prior to their usage. In this work, we present a novel approach, named Radiosounding HARMonization (RHARM), providing a homogenized data set of temperature, humidity and wind profiles along with an estimation of the measurement uncertainties for 697 radiosounding stations globally. The RHARM method has been used to adjust twice daily (0000 and 1200 UTC) radiosonde data holdings at 16 pressure levels in the range 1,000–10 hPa, from 1978 to present, provided by the Integrated Global Radiosonde Archive. Relative humidity (RH) data are limited to 250 hPa. The applied adjustments are interpolated to all reported levels. RHARM is the first data set to provide homogenized time series with an estimation of the observational uncertainty at each sounding pressure level. By construction, RHARM adjusted fields are not affected by cross-contamination of biases across stations and are fully independent of reanalysis data. Analysis of trends for temperature, RH and winds highlights increased geographical coherency of trends over 1978–2000 globally, but especially in the Northern Hemisphere and South America. RHARM shows warming trends of 0.39 K/decade at 300 hPa in the Northern Hemisphere and of 0.25 K/ decade in the tropics. The RHARM adjustments also reduce differences with the European Centre for Medium-Range Weather Forecast ERA5 reanalysis, with the strongest effect in the Northern Hemisphere for temperature and relative humidity. For wind speed, the comparison indicates a good agreement with ERA5 in the troposphere.

The RHARM (Radiosounding HARMonization) algorithm is the first to provide homogenized radiosonde-based records of temperature, relative humidity and wind profiles since 1978, alongside an estimatio...

In December 2019, a contract between CNR and ECMWF was signed for a pilot ACTRIS/EARLINET data provision to the Copernicus Atmosphere Monitoring Service (CAMS). Such pilot contract (CAMS21b) aims to put in place a first data provision for a set of selected stations and it will demonstrate the feasibility of fully traceable and quality-controlled data provision for the whole network.In CAMS21b, the main effort is devoted to design, test and set up the provision of quality-controlled ACTRIS/EARLINET products in Real Real Time (RRT) and/or Near Real Time (NRT) to CAMS. The activities are focused on the automatic centralized data processing and data provision, ensuring the full traceability of the products from the data acquisition level up to the final quality-controlled data level. Most of the activities are done at ARES, the EARLINET/ACTRIS data center node at CNR, for assuring the centralized, harmonized and quality-controlled processing in compliance with FAIR principles.New modules and submodules of the ACTRIS/EARLINET Single Calculus Chain (SCC) as well as optimized algorithms for cloud screening have been designed. Additional procedures were implemented for improving the quality of the data provided in NRT, but also for the quality control of the Level 2 products which are delivered with a time delay.The release of a new version of SCC and of QC procedure is planned for mid-February.The data provision started in October 2020 at the test site of Potenza. A system has been set up for measurement reporting and monitoring of KPIs (Key Performance Indicators). After 3 months of measurements, the overall data provision system showed no critical points.In January 2021, the provision started for a group of 9 stations which are seen as representative for the whole network in terms of instrumental capability, but also ensuring a good geographical coverage of the European continent.In order to accommodate also measurements from non-continuous operation systems, a measurement schedule has been set up, compromising between the need of a large number of measurements and costs/efforts at each station. The measurement schedule has been designed through a representativeness study and foresees 6 slots of measurements per week, 3 in daytime and 3 in nighttime conditions.The successful implementation of the pilot allows the provision of aerosol optical property profiles to the CAMS services. from a set of observational sites distributed over the different European regions. These profiles is expected to be of interest for the assimilation, near real time evaluation and re-analysis evaluation of several CAMS products, including the aerosol load over Europe for air quality issues, atmospheric composition, climate forcing and solar and UV products. This allows for having a systematic solution for looking into specific events as they develop (e.g. the dust plume that you investigated earlier this month or the Californian fires in September), supporting or contradicting model forecasts. This pilot is the first provision of aerosol profiles from a high-quality ground-based network in NRT for this kind of applications. It is expected that these efforts will be continued in the next phase of CAMS/Copernicus (2021-2027).

Observational records are essential for assessing long-term changes in our climate. However, these records are more often than not influenced by residual non‐climatic factors which must be detected and adjusted prior to their usage. Ideally, measurement uncertainties should be properly quantified and validated. In the context of the Copernicus Climate Change Service (C3S), a novel approach, named RHARM (Radiosounding HARMonization), has been developed to provide a harmonized dataset of temperature, humidity and wind profiles along with an estimation of the measurement uncertainties for about 650 radiosounding stations globally. The RHARM method has been applied to IGRA daily (0000 and 1200 UTC) radiosonde data holdings on 16 standard pressure levels (from 1000 to 10 hPa) from 1978 to present. Relative humidity adjustment and data provision has been limited to 250 hPa owing to pervasive issues on sensors' performance in the upper troposphere and lower stratosphere. The applied adjustments are interpolated to all reported significant levels to retain information content contained within each individual ascent profile. Each historical station time series is harmonized using two distinct methods. Firstly, the most recent period of the records when modern radiosonde models have been in operation at each station (typically starting between 2004 and 2010 but varying on a station-by-station basis) are post-processed and adjusted using reference datasets from the GCOS Reference Upper Air Network (GRUAN) and from the 2010 WMO/CIMO (World Meteorological Organization/Commission for Instruments and Methods of Observation) radiosonde intercomparison. Subsequently, at each mandatory pressure level, the remaining historical data are scanned backward in time to detect structural breaks due to prolonged systematic effects in the measurements and then adjusted to homogenize the time series. This paper describes the dataset portion related to the adjustment of post-2004 measurements only. A step-by-step description of the algorithm is reported and comparisons with GRUAN and atmospheric reanalysis data for temperature and relative humidity data are discussed. The evaluation shows that the strongest benefit of RHARM compared to existing products is related to the substantive adjustments applied to relative humidity time series for values below 15 % and above 55 % as well as to the provision of the uncertainties for all variables. Uncertainties have been validated using the ECMWF reanalysis short-range forecast outputs. The RHARM algorithm is the first to provide homogenized time series of temperature, relative humidity and wind profiles alongside an estimation of the observational uncertainty for each single observation at each pressure level. A subset of RHARM dataisavailable at https://doi.org/10.5281/zenodo.3973353 (Madonnaet al., 2020a)

Upper-air radiosounding observations of temperature, relative humidity and wind are a of the primary data source for climate studies. Nevertheless, historical radiosounding time series are affected by several systematic uncertainties due to change in the measurement sensors. In the frame of the Copernicus Climate Change Service (C3S), a novel approach, named RHARM (Radiosounding HARMonization), has been developed to homogenize temperature, humidity and wind radiosounding profile time series available from the the Integrated Global Radiosonde Archive (IGRA) and provide an estimation of the total uncertainty for each single profile. RHARM is an alternative to the few existing approaches.RHARM is applied to daily (0000 and 1200 UTC) radiosonde data on 16 standard pressure levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) for the IGRA data from 1978 onward. Relative humidity (RH) adjustments is limited to 300 hPa owing to pervasive sensor performance issues at greater altitudes. The bias adjustments are estimated at mandatory levels only but they are also interpolated to the significant levels reported within each individual ascent profile.This paper discusses the comparison of the monthly anomalies and trends estimated at different latitudes and pressure levels for ERA5, IGRA and RHARM. Trends are esitmated using a robust least absolute deviation method. ERA5 is the latest climate reanalysis produced by ECMWF providing hourly data on on regular latitude-longitude grids at 0.25° x 0.25° resolution, with atmospheric parameters on 137 pressure levels (available on https://cds.climate.copernicus.eu).Differences in the comparisons among the three datasets will be discussed along with the analysis of the trends observed in the considered time series. To evaluate its homogeneity and stability, the uncertainty estimation provided in RHARM will be also compared with O-B field obtained using the to ECWMF operational forecast model as the background.

Climate trends estimated using historical radiosounding time series may be significantly affected by the choice of the regression method to use, as well as by a subsampling of the dataset often adopted in specific applications. These are contributions to the uncertainty of trend estimations, which have been quantified in literature, although on specific pairs of regression methods, and in the not very recent past characterized by smaller trends in temperature than those observed over the last two decades. This paper investigates the sensitivity of trend estimations to four linear regression methods (parametric and nonparametric) and to the artificial subsampling of the same dataset using historical radiosounding time series from 1978 onwards, available in the version 2 of the Integrated Global Radiosonde Archive (IGRA). Results show that long-term decadal trends may have not negligible uncertainties related to the choice of the regression method, the percentage of data available, the amount of missing data and the number of stations selected in the dataset. The choice of the regression methods increases uncertainties in the decadal trends ranging from -0.10 to -0.01 K?da-1 for temperature in the lower stratosphere at 100 hPa and from 0.2 to 0.8% da-1 for relative humidity (RH) in the middle troposphere at 300 hPa. Differences can also increase up to 0.4 K?da-1 at 300 hPa when the amount of missing data exceeds 50% of the original dataset for temperature, while for RH, significant differences are observed in the lower troposphere at 925 hPa for almost all datasets. Finally, subsampling effects on trend estimation are quantified by artificially reducing the size of the IGRA dataset: Results show that subsampling effects on trend estimations when at least 60 stations, up to 76% of data available, are considered for temperature and at least 40 stations for RH.

Bisphenol A (BPA) is a synthetic compound widely used for the production of polycarbonate plasticware and epoxy resins. BPA exposure is widespread and more than 90% of individuals have detectable amounts of the molecule in their body fluids, which originates primarily from diet. Here, we investigated whether prenatal exposure to BPA affects the mevalonate (MVA) pathway in rat brain fetuses, and whether potential effects are sex-dependent. The MVA pathway is important for brain development and function. Our results demonstrate that the fetal brain, exposed in utero to a very low dose of BPA (2.5 &micro, g/kg/day), displayed altered MVA pathway activation, increased protein prenylation, and a decreased level of pro-BDNF. Interestingly, the BPA-induced effects on estrogen receptor &alpha, were sex-dependent. In conclusion, this work demonstrates intergenerational effects of BPA on the brain at very low doses. Our results reveal new targets for BPA-induced interference and underline the impacts of BPA on health.